from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 10 predictions (41.67%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
4	KNeighborsClassifier_brute_force	predict	0.067	100000	1000	100	3.105810	0.045852	NaN	0.000258	0.003106	brute	-1	5	0.743	0.226007	0.001248	0.676	13.742109	13.742319
5	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.024820	0.002282	NaN	0.000032	0.024820	brute	-1	5	1.000	0.008910	0.000214	0.000	2.785486	2.786289
7	KNeighborsClassifier_brute_force	predict	0.103	100000	1000	100	2.384116	0.005277	NaN	0.000336	0.002384	brute	1	100	0.846	0.229251	0.002502	0.743	10.399586	10.400205
13	KNeighborsClassifier_brute_force	predict	0.103	100000	1000	100	2.375006	0.004540	NaN	0.000337	0.002375	brute	1	5	0.743	0.282499	0.000893	0.846	8.407146	8.407188
16	KNeighborsClassifier_brute_force	predict	0.067	100000	1000	100	1.251740	0.016235	NaN	0.000639	0.001252	brute	1	1	0.676	0.229929	0.000688	0.743	5.444032	5.444057
17	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.019407	0.000194	NaN	0.000041	0.019407	brute	1	1	0.000	0.008780	0.000231	1.000	2.210377	2.211141
22	KNeighborsClassifier_brute_force	predict	0.038	100000	1000	2	2.963670	0.021615	NaN	0.000005	0.002964	brute	-1	5	0.883	0.036591	0.000125	0.845	80.994390	80.994865
25	KNeighborsClassifier_brute_force	predict	0.004	100000	1000	2	2.310522	0.001777	NaN	0.000007	0.002311	brute	1	100	0.887	0.038284	0.000144	0.883	60.351726	60.352153
31	KNeighborsClassifier_brute_force	predict	0.004	100000	1000	2	2.337245	0.027438	NaN	0.000007	0.002337	brute	1	5	0.883	0.086500	0.002885	0.887	27.020217	27.035245
34	KNeighborsClassifier_brute_force	predict	0.038	100000	1000	2	1.113005	0.007140	NaN	0.000014	0.001113	brute	1	1	0.845	0.041464	0.004027	0.883	26.842386	26.968699

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.727	0.0	-1	1	0.050	0.0	0.239	0.239	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.786	0.0	-1	5	0.049	0.0	0.239	0.239	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.916	0.0	1	100	0.049	0.0	0.234	0.234	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.883	0.0	-1	100	0.049	0.0	0.235	0.235	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.911	0.0	1	5	0.049	0.0	0.235	0.235	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.915	0.0	1	1	0.050	0.0	0.233	0.233	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.315	0.0	-1	1	0.010	0.0	0.513	0.513	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.317	0.0	-1	5	0.010	0.0	0.512	0.512	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.317	0.0	1	100	0.010	0.0	0.514	0.514	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.319	0.0	-1	100	0.010	0.0	0.504	0.504	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.318	0.0	1	5	0.010	0.0	0.513	0.513	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.313	0.0	1	1	0.010	0.0	0.513	0.513	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.017	0.041	0.000	0.002	-1	1	0.227	0.001	8.894	8.894	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.003	0.000	0.023	-1	1	0.009	0.000	2.630	2.630	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.106	0.046	0.000	0.003	-1	5	0.226	0.001	13.742	13.742	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.002	0.000	0.025	-1	5	0.009	0.000	2.785	2.786	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.384	0.005	0.000	0.002	1	100	0.229	0.003	10.400	10.400	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	0.000	0.021	1	100	0.009	0.000	2.320	2.322	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.119	0.050	0.000	0.003	-1	100	0.285	0.013	10.952	10.963	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.004	0.000	0.026	-1	100	0.010	0.000	2.527	2.529	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.375	0.005	0.000	0.002	1	5	0.282	0.001	8.407	8.407	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	0.000	0.020	1	5	0.010	0.002	1.973	2.014	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.252	0.016	0.001	0.001	1	1	0.230	0.001	5.444	5.444	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	1	0.009	0.000	2.210	2.211	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.744	0.032	0.000	0.002	-1	1	0.037	0.000	47.682	47.683	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.008	0.002	0.000	0.008	-1	1	0.001	0.000	8.861	8.907	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.964	0.022	0.000	0.003	-1	5	0.037	0.000	80.994	80.995	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.011	0.005	0.000	0.011	-1	5	0.001	0.000	11.624	11.658	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.311	0.002	0.000	0.002	1	100	0.038	0.000	60.352	60.352	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	0.000	0.004	1	100	0.001	0.000	3.899	3.929	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.991	0.038	0.000	0.003	-1	100	0.085	0.000	34.993	34.993	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.010	0.003	0.000	0.010	-1	100	0.001	0.000	10.190	10.239	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.337	0.027	0.000	0.002	1	5	0.086	0.003	27.020	27.035	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	0.000	0.004	1	5	0.001	0.000	3.484	3.501	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.113	0.007	0.000	0.001	1	1	0.041	0.004	26.842	26.969	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.293	2.305	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 8 predictions (33.33%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	1.699014	0.008849	NaN	0.000047	0.001699	kd_tree	1	5	0.975	0.110405	0.001272	0.964	15.388909	15.389930
4	KNeighborsClassifier_kd_tree	predict	0.002	1000000	1000	10	0.967488	0.011364	NaN	0.000083	0.000967	kd_tree	-1	5	0.975	0.588889	0.010649	0.973	1.642905	1.643174
10	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	0.891906	0.015064	NaN	0.000090	0.000892	kd_tree	1	1	0.964	0.195108	0.002086	0.975	4.571348	4.571609
13	KNeighborsClassifier_kd_tree	predict	0.002	1000000	1000	10	5.389894	0.058023	NaN	0.000015	0.005390	kd_tree	1	100	0.973	0.203053	0.002403	0.975	26.544230	26.546089
19	KNeighborsClassifier_kd_tree	predict	0.028	1000	1000	2	0.022982	0.000167	NaN	0.000696	0.000023	kd_tree	1	5	0.923	0.000513	0.000117	0.895	44.772734	45.921049
22	KNeighborsClassifier_kd_tree	predict	0.004	1000	1000	2	0.025739	0.000489	NaN	0.000622	0.000026	kd_tree	-1	5	0.923	0.005467	0.000134	0.919	4.708290	4.709702
28	KNeighborsClassifier_kd_tree	predict	0.028	1000	1000	2	0.021302	0.000252	NaN	0.000751	0.000021	kd_tree	1	1	0.895	0.000914	0.000136	0.923	23.298220	23.553737
31	KNeighborsClassifier_kd_tree	predict	0.004	1000	1000	2	0.039018	0.000268	NaN	0.000410	0.000039	kd_tree	1	100	0.919	0.000862	0.000168	0.923	45.241585	46.088950

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.599	0.045	0.022	0.0	1	5	0.822	0.008	4.381	4.381	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.126	0.068	0.019	0.0	-1	5	0.815	0.012	5.060	5.060	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.038	0.068	0.020	0.0	-1	1	0.803	0.013	5.028	5.029	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.017	0.079	0.020	0.0	1	1	0.781	0.005	5.142	5.142	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.268	0.045	0.019	0.0	1	100	0.827	0.039	5.161	5.167	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.053	0.063	0.020	0.0	-1	100	0.793	0.007	5.113	5.113	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.024	0.0	1	5	0.009	0.012	0.077	0.132	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	-1	5	0.002	0.002	0.273	0.359	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	-1	1	0.003	0.002	0.220	0.260	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.024	0.0	1	1	0.001	0.000	0.550	0.552	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.025	0.0	1	100	0.001	0.000	0.543	0.548	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	-1	100	0.001	0.000	0.560	0.563	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.699	0.009	0.000	0.002	1	5	0.110	0.001	15.389	15.390	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	5.685	6.047	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.967	0.011	0.000	0.001	-1	5	0.589	0.011	1.643	1.643	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.001	0.000	4.868	5.049	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.512	0.007	0.000	0.001	-1	1	0.113	0.002	4.543	4.544	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	10.173	11.062	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.892	0.015	0.000	0.001	1	1	0.195	0.002	4.571	4.572	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	4.452	4.717	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.390	0.058	0.000	0.005	1	100	0.203	0.002	26.544	26.546	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	1	100	0.000	0.000	11.128	11.677	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2.928	0.024	0.000	0.003	-1	100	0.580	0.003	5.050	5.050	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	8.542	8.825	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.000	0.001	0.000	1	5	0.001	0.000	44.773	45.921	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.193	6.745	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.026	0.000	0.001	0.000	-1	5	0.005	0.000	4.708	4.710	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	17.261	20.874	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.000	0.001	0.000	-1	1	0.001	0.000	42.358	43.530	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	21.264	26.307	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.000	0.001	0.000	1	1	0.001	0.000	23.298	23.554	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.626	6.946	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.039	0.000	0.000	0.000	1	100	0.001	0.000	45.242	46.089	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.644	6.923	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.040	0.000	0.000	0.000	-1	100	0.006	0.000	7.235	7.250	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	18.322	22.195	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.654	0.006	30	0.024	0.0	k-means++	0.505	0.038	1.295	1.298	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.572	0.003	30	0.028	0.0	random	0.543	0.032	1.053	1.055	See	See
6	KMeans_tall	fit	1000000	1000000	100	6.768	0.056	30	0.118	0.0	k-means++	2.936	0.009	2.305	2.305	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.466	0.031	30	0.124	0.0	random	3.097	0.014	2.088	2.088	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.362	1.08	30	0.000	0.000	k-means++	0.0	0.0	2024.555	2262.061	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.00	30	0.000	0.002	k-means++	0.0	0.0	12.041	14.273	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.00	30	0.010	0.000	random	0.0	0.0	9.275	10.607	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.00	30	0.000	0.002	random	0.0	0.0	12.030	14.375	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.00	30	0.403	0.000	k-means++	0.0	0.0	6.428	7.155	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.00	30	0.001	0.002	k-means++	0.0	0.0	10.103	11.591	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.00	30	0.399	0.000	random	0.0	0.0	6.216	6.599	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.00	30	0.001	0.002	random	0.0	0.0	10.636	12.186	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 3 predictions (37.5%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	function	diff_adjusted_rand_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
4	KMeans_short	predict	0.001559	10000	1000	2	0.002192	0.000116	20	0.007300	0.000002	full	random	20	300	1	1.000000e-16	0.000139	0.000631	0.000106	0.001698	3.473654	3.521957
7	KMeans_short	predict	0.006417	10000	1000	100	0.003105	0.000169	20	0.257631	0.000003	full	k-means++	20	300	1	1.000000e-16	0.301489	0.001320	0.000135	0.295073	2.352137	2.364369
10	KMeans_short	predict	0.039683	10000	1000	100	0.003116	0.000141	20	0.256778	0.000003	full	random	20	300	1	1.000000e-16	0.259606	0.001261	0.000130	0.299288	2.469968	2.483114

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.289	0.003	20	0.001	0.0	k-means++	0.102	0.001	2.839	2.840	See	See
3	KMeans_short	fit	10000	10000	2	0.103	0.000	20	0.002	0.0	random	0.035	0.000	2.968	2.969	See	See
6	KMeans_short	fit	10000	10000	100	0.755	0.007	20	0.011	0.0	k-means++	0.391	0.004	1.931	1.931	See	See
9	KMeans_short	fit	10000	10000	100	0.260	0.004	20	0.031	0.0	random	0.145	0.003	1.796	1.796	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.007	0.000	k-means++	0.001	0.0	3.499	3.541	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	11.614	13.640	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.007	0.000	random	0.001	0.0	3.474	3.522	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	random	0.000	0.0	11.627	13.588	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.258	0.000	k-means++	0.001	0.0	2.352	2.364	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	8.590	9.413	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.257	0.000	random	0.001	0.0	2.470	2.483	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	random	0.000	0.0	8.585	9.413	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.985	0.045	[20]	0.067	0.000	2.092	0.008	5.730	5.730	See	See
3	LogisticRegression	fit	1000	1000	10000	0.824	0.017	[27]	0.097	0.001	0.774	0.036	1.064	1.065	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.364	0.0	0.000	0.0	0.797	0.829	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.010	0.0	0.000	0.0	0.406	0.452	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[27]	4.164	0.0	0.003	0.0	0.583	0.585	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[27]	0.774	0.0	0.001	0.0	0.128	0.131	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.200	0.001	0.400	0.0	0.206	0.001	0.971	0.971	See	See
3	Ridge	fit	1000000	1000000	100	1.211	0.023	0.661	0.0	0.254	0.002	4.771	4.771	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	7.913	0.0	0.017	0.0	0.600	0.600	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	1.011	0.0	0.000	0.0	0.565	0.622	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	4.531	0.0	0.000	0.0	0.652	0.701	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	0.012	0.0	0.000	0.0	0.670	0.771	See	See